import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


_SCREAMING_SNAKE_CASE 		=      """sshleifer/mar_enro_6_3_student"""
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    super().setUp()

    UpperCamelCase			  =      cached_path(
        """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""   ,   extract_compressed_file=lowerCamelCase_   ,   )
    UpperCamelCase			  =      f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""







   @slow
   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    MarianMTModel.from_pretrained(lowerCamelCase_				)







   @slow
   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """$MAX_LEN""": 64,
        """$BS""": 64,
        """$GAS""": 1,
        """$ENRO_DIR""": self.data_dir,
        """facebook/mbart-large-cc25""": MARIAN_MODEL,
        # "val_check_interval=0.25": "val_check_interval=1.0",
        """--learning_rate=3e-5""": """--learning_rate 3e-4""",
        """--num_train_epochs 6""": """--num_train_epochs 1""",
    }

    # Clean up bash script
    UpperCamelCase			  =      (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""				)[1].strip()
    UpperCamelCase			  =      bash_script.replace("""\\\n"""   ,   """"""				).strip().replace("""\"$@\""""   ,   """"""				)
    for k, v in env_vars_to_replace.items():
     UpperCamelCase			  =      bash_script.replace(lowerCamelCase_   ,   str(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()

    # bash_script = bash_script.replace("--fp16 ", "")
    UpperCamelCase			  =      f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
    # XXX: args.gpus > 1 : handle multi_gpu in the future

    UpperCamelCase			  =      ["""finetune.py"""] + bash_script.split() + args
    with patch.object(lowerCamelCase_   ,   """argv"""   ,   lowerCamelCase_				):
     UpperCamelCase			  =      argparse.ArgumentParser()
     UpperCamelCase			  =      pl.Trainer.add_argparse_args(lowerCamelCase_				)
     UpperCamelCase			  =      SummarizationModule.add_model_specific_args(lowerCamelCase_   ,   os.getcwd()				)
     UpperCamelCase			  =      parser.parse_args()
     UpperCamelCase			  =      main(lowerCamelCase_				)

    # Check metrics
    UpperCamelCase			  =      load_json(model.metrics_save_path				)
    UpperCamelCase			  =      metrics["""val"""][0]
    UpperCamelCase			  =      metrics["""val"""][-1]
    self.assertEqual(len(metrics["""val"""]				)   ,   (args.max_epochs / args.val_check_interval)				)
    assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]   ,   lowerCamelCase_				)

    self.assertGreater(last_step_stats["""val_avg_gen_time"""]   ,   0.0_1				)
    # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
    self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]   ,   1.0				)

    # test learning requirements:

    # 1. BLEU improves over the course of training by more than 2 pts
    self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]   ,   2				)

    # 2. BLEU finishes above 17
    self.assertGreater(last_step_stats["""val_avg_bleu"""]   ,   17				)

    # 3. test BLEU and val BLEU within ~1.1 pt.
    self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]				)   ,   1.1				)

    # check lightning ckpt can be loaded and has a reasonable statedict
    UpperCamelCase			  =      os.listdir(lowerCamelCase_				)
    UpperCamelCase			  =      [x for x in contents if x.endswith(""".ckpt"""				)][0]
    UpperCamelCase			  =      os.path.join(args.output_dir   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.load(lowerCamelCase_   ,   map_location="""cpu"""				)
    UpperCamelCase			  =      """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
     UpperCamelCase			  =      {os.path.basename(lowerCamelCase_				) for p in contents}
     assert "test_generations.txt" in contents
     assert "test_results.txt" in contents
     # assert len(metrics["val"]) ==  desired_n_evals
     assert len(metrics["""test"""]				) == 1





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   @timeout_decorator.timeout(600				)
   @slow
   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
    UpperCamelCase			  =      {
        """--fp16_opt_level=O1""": """""",
        """$MAX_LEN""": 128,
        """$BS""": 16,
        """$GAS""": 1,
        """$ENRO_DIR""": data_dir,
        """$m""": """sshleifer/student_marian_en_ro_6_1""",
        """val_check_interval=0.25""": """val_check_interval=1.0""",
    }

    # Clean up bash script
    UpperCamelCase			  =      (
        (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""				)[1].strip()
    )
    UpperCamelCase			  =      bash_script.replace("""\\\n"""   ,   """"""				).strip().replace("""\"$@\""""   ,   """"""				)
    UpperCamelCase			  =      bash_script.replace("""--fp16 """   ,   """ """				)

    for k, v in env_vars_to_replace.items():
     UpperCamelCase			  =      bash_script.replace(lowerCamelCase_   ,   str(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      bash_script.replace("""--fp16"""   ,   """"""				)
    UpperCamelCase			  =      6
    UpperCamelCase			  =      (
        ["""distillation.py"""]
        + bash_script.split()
        + [
            f"""--output_dir={output_dir}""",
            """--gpus=1""",
            """--learning_rate=1e-3""",
            f"""--num_train_epochs={epochs}""",
            """--warmup_steps=10""",
            """--val_check_interval=1.0""",
            """--do_predict""",
        ]
    )
    with patch.object(lowerCamelCase_   ,   """argv"""   ,   lowerCamelCase_				):
     UpperCamelCase			  =      argparse.ArgumentParser()
     UpperCamelCase			  =      pl.Trainer.add_argparse_args(lowerCamelCase_				)
     UpperCamelCase			  =      SummarizationDistiller.add_model_specific_args(lowerCamelCase_   ,   os.getcwd()				)
     UpperCamelCase			  =      parser.parse_args()
     # assert args.gpus == gpus THIS BREAKS for multi_gpu

     UpperCamelCase			  =      distill_main(lowerCamelCase_				)

    # Check metrics
    UpperCamelCase			  =      load_json(model.metrics_save_path				)
    UpperCamelCase			  =      metrics["""val"""][0]
    UpperCamelCase			  =      metrics["""val"""][-1]
    assert len(metrics["""val"""]				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

    assert last_step_stats["val_avg_gen_time"] >= 0.0_1

    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
    assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]   ,   lowerCamelCase_				)

    # check lightning ckpt can be loaded and has a reasonable statedict
    UpperCamelCase			  =      os.listdir(lowerCamelCase_				)
    UpperCamelCase			  =      [x for x in contents if x.endswith(""".ckpt"""				)][0]
    UpperCamelCase			  =      os.path.join(args.output_dir   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.load(lowerCamelCase_   ,   map_location="""cpu"""				)
    UpperCamelCase			  =      """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
     UpperCamelCase			  =      {os.path.basename(lowerCamelCase_				) for p in contents}
     assert "test_generations.txt" in contents
     assert "test_results.txt" in contents
     # assert len(metrics["val"]) ==  desired_n_evals
     assert len(metrics["""test"""]				) == 1

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
